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D084 - CHEMICAL, ENVIRONMENTAL, BIOMEDICAL, HYDRAULIC AND MATERIALS ENGINEERING

XXXVIII

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PhD Student

Background and Research Project

Graphical Abstract 

 

Amato Emanuele

I’m Emanuele Amato, PhD student in “Chemical, Environmental, Biomedical, Hydraulic and Materials Engineering” at the University of Palermo. I got my master’s degree in Civil Engineering (LM-23) curriculum Hydraulics on October 2022. My research project concerns the design of a telescopic wind turbine with a reduced environmental impact within the scope of the project “PERIMA 2”. This tower, which is raised and lowered by automation or by remote control, allows differentiation of the presence of the generator within the landscape over time. The objective of the research is the choice of the best pile configuration depending on the boundary conditions such as: dynamic action caused by earthquake and wind, foundation type and associated construction costs. This will lead to the selection of the best configuration for the site where the pole is to be installed, but at the same time lay the foundation for the creation of a system independent of site-specific conditions.

 

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Avanti Marco

Marco Avanti is an Environmental Scientist, a Civil and Environmental Engineer and a Ph.D. student in “Chemical, Environmental, Biomedical, Hydraulic, and Materials Engineering”. He obtained his master's degree by conducting a study to address the longstanding and increasingly frequent phenomenon of flooding in the Mondello neighborhood with his Master’s thesis, titled "Mitigation of Flooding in the Northern Zone of Palermo: Analysis and Intervention Proposals." His doctoral research aim to improve understanding of urban flood dynamics and enhance hydraulic models for flood management. It seeks to refine assessment methods for urban flood vulnerability, taking into account the growing impact of extreme weather events and uncontrolled urbanization. Outcomes of this project aim to advance in hydraulic modelling in urban areas and flood risk management, providing crucial tools and insights for the design of resilient and safer cities.

 

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Baccarella Marta

Marta Baccarella is a PhD Student of the XXXVIII cycle of the PhD course in "Chemical, Environmental, Biomedical, Hydraulic and Materials Engineering" at the University of Palermo in collaboration with Ri.MED Foundation. The PhD project is part of the D.M. n° 352 relating to Ph.D. scholarships 50% funded by the MUR from the PNRR and 50% funded by companies. She has a Bachelor's degree in Biomedical Engineering - Biomaterials and medical devices and a Master's degree in Biomedical engineering - biomechanical and medical devices, both earned at the University of Palermo in 2020 and 2022 respectively. During her master's thesis, she spent a 6 months internship in Livanova-Sorin Group at Mirandola (MO) with a project entitled “Multiscale modeling of platelet activation in membrane oxygenation systems”. Her scientific activity is about bio-fabrication methods and computational approaches applied to cardiovascular tissue engineering scaffolds. In particular, Marta’s project goal is to identify an optimal size and shape pattern that enhances the growth of a functional endothelial cell layer. The project consists of two different steps. The first one is a 2D and 3D topological analysis to evaluate the structure of the native endothelium through imaging tools such as a scanning electron microscope and a multiphoton microscope. The second phase consists of advanced bio-fabrication techniques such as electrospinning and soft lithography in order to replicate the cellular pattern.

 

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 (n.a.)

Beikahmadi Niloufar

I am Niloufar Beikahmadi, PhD student in “Chemical, Environmental, Biomedical, Hydraulic and Materials Engineering” at the University of Palermo. I got my bachelor’s and master’s degree in civil engineering (water science). My research project focus on bias adjustment and downscaling of hydroclimatological products, which involves remote sensing observations, reanalysis data and the ensemble climate model outputs. Research objectives encompass a comprehensive multiphasestudy, ranging from the multiscale analysis of product performance across the Mediterranean region to the creation of a bias-corrected and super-resolution ATLAS spanning 85 years. To achieve these goals, I employ probabilistic methods and statistical approaches, integrating advanced frameworks such as deep learning models for sequential regression and pattern recognition.

 

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Castiglione Maria

I am Maria Castiglione, P.h.D. student in “Chemical, Environmental, Biomedical, Hydraulic and Materials Engineering”. I graduated with a master's degree in Engineering and Innovative Technologies for the Environment (LM-35) at the University of Palermo. My research project aims to evaluate the risks associated with drinking water and wastewater treatment plants following natural events, intensified by climate change and subsequently evaluate robust and recoverable strategies. The methodology could help to address a gap in the existing planning and risk instruments, increasing the awareness of the local planners about the unexpected effects of multiple risks and providing an essential indication of the priority areas to address technical studies and financial resources. This study is carried out within the RETURN Extended Partnership and receive funding from the European Union Next-Generation EU.

 

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De Caro Dario

Dario De Caro is a Ph.D. student in “Chemical, Environmental, Biomedical, Hydraulic and Materials Engineering”. He gained a Bachelor and a Master’s degree in in Environmental Engineering at the University of Palermo in July 2018 and July 2020, respectively. He deepened his knowledge of environmental issues related to hydraulic and hydrologic applications in agriculture, working as a research fellow at the University of Palermo since March 2021. His research project is based on the estimation of evapotranspiration fluxes in Mediterranean agro-ecosystems at different spatial scales. Agro-hydrological and/or energy balance models will be implemented in the framework of Agriculture 4.0 by merging data from in-situ smart sensors, remote sensing, and machine learning techniques.

 

Link to CV

Graphical Abstract